It is shown that the performance of image coding techniques can be enhanced via the utilization of a priori knowledge. Critical features of the image are first identified and then they are accounted for more favorably in the coding process. For satellite imagery, thin lines and point objects constitute critical features of interest whose preservation in the coding process is crucial. For a human visual system, the impact of the coding degradation at low rates is much more detrimental for these features than for the edges which constitute boundaries between regions of different contrasts. A highly non-linear, matched filter-based algorithm to detect such features has been developed. Pre-enhancement (highlighting) of the detected features within the image prior to coding is shown to noticeably reduce the severity of the coding degrada-tion. A yet more robust approach is the pre-enhancement of the slightly smoothed image. This operation gives rise to an image in which all critical thin lines and point ojects are crisp and well-defined at the cost of non-essential edges of the image being slightly rounded off. For the transform coding techniques, distortion parameter readjustment and variable-block size coding provide promising alternatives to the pre-enhancement ap-proaches. In the former, the sub-blocks containing any part of the detected critical features are kept within a low distortion bound via the local rate adjustment mechanism. The latter approach is similar to the former except that the image is partitioned into varying size sub-blocks based on the extracted feature map.